Method an apparatus for tracking motion using radio frequency signals

ABSTRACT

Techniques for a motion tracing device using radio frequency signals are presented. The motion tracing device utilizes radio frequency signals, such as WiFi to identify moving objects and trace their motion. Methods and apparatus are defined that can measure multiple WiFi backscatter signals and identify the backscatter signals that correspond to moving objects. In addition, motion of a plurality of moving objects can be detected and traced for a predefined duration of time.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present invention claims priority to U.S. provisional application No. 61/987,790, filed May 2, 2014, entitled “Method And Apparatus For Tracing Motion Using Radio Frequency Signals.” The present invention is related to U.S. application Ser. No. 14/456,807, filed Aug. 8, 2014, entitled “Full Duplex Radios,” and Application No. PCT/US2014/065814, filed Nov. 14, 2014, entitled “Backscatter Estimation Using Progressive Self Interference Cancellation,” the contents of which are incorporated herein by reference in their entirety.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with Government support under contract 0832820 awarded by the National Science Foundation. The Government has certain rights in this invention.

FIELD OF THE INVENTION

The present invention relates to measuring systems and methods for detecting motion, and more particularly to a system and method for detecting and/or tracing motion using radio signals.

BACKGROUND OF THE INVENTION

Many devices are available in the market for detecting movement of objects in an environment. For example, motion sensors may be used in indoor and/or outdoor environments to turn lights on and/or off. In addition, motion sensors can be used in security applications. Recently, cameras are used to detect hand gestures in front of a computer/smartphone. However, these cameras are unable to detect motion in low-light or dark environments. In general, motion detection can have many applications, such as healthcare, security, home efficiency and the like. Detecting motion efficiently in a variety of environments remains a challenge.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example home scenario, in which Wi-Fi signals are scattered by objects, according to one embodiment of the present invention.

FIG. 2 illustrates an example object detection system, according to one embodiment of the present invention.

FIG. 3 illustrates an example high level block diagram of a motion tracing device, according to one embodiment of the present invention.

FIG. 5 illustrates example operations that may be performed to detect and track a moving object, according to one embodiment of the present invention.

FIG. 4 illustrates an example block diagram of a motion detection/tracing system, according to one embodiment of the present invention.

FIGS. 6A, 6B and 6C illustrate an example backscatter channel composed of three impulses, according to one embodiment of the present invention.

FIGS. 7A through 7D sequentially illustrate intermediate results of an estimation of the backscatter signal after performing the progressive self-interference cancellation, according to one embodiment of the present invention.

FIGS. 8A, 8B and 8C illustrate example backscatter components obtained from a simulated hand movement in a typical indoor scenario using a Wireless tracing software, according to one embodiment of the present invention.

FIGS. 9A and 9B illustrate an example application of Hungarian algorithm for a subset of backscatter components, according to one embodiment of the present invention.

FIGS. 10A and 10B illustrate an example movement of a human hand, according to one embodiment of the present invention.

FIG. 11 illustrates example operations that may be performed by a device to detect motion, in accordance with one embodiment of the present disclosure.

FIG. 12 illustrates example operations that may be performed by a device to trace motion of a moving device, in accordance with one embodiment of the present disclosure.

FIG. 13 is a simplified block diagram of an exemplary computer or data processing system 1300 in which portions of the motion tracing/detection device, may be embodied.

SUMMARY

In one embodiment, a method for detecting motion in an environment is disclosed. The method includes, in part, transmitting a radio frequency signal, receiving a composite signal comprising a plurality of backscatter signals. The plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment. The one or more objects include at least one moving object and one or more static objects. The method further includes, in part, detecting the at least one moving object in accordance with the composite signal. The method includes, in part, estimating one or more parameters corresponding to a plurality of backscatter signals corresponding to the at least one moving object. The one or more parameters are estimated by progressively removing one or more backscatter signals corresponding to the one or more static objects from the composite signal.

In one embodiment, a method for tracing motion is disclosed. The method includes, in part, receiving information corresponding to one or more moving objects from one or more devices. The information includes one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects. The method further includes, in part, estimating location of at least one of the one or more moving objects in accordance with the received information from the one or more devices.

The method further includes identifying a plurality of paths corresponding to the at least one moving object in accordance with the received information, determining one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object, and determining a number of moving objects corresponding to the number of motion categories.

In one embodiment, an apparatus for detecting motion in an environment is disclosed. The apparatus includes, in part, a transmitter, one or more receivers, a memory, and at least one processor coupled to the transmitter, the one or more receivers and the memory. The at least one processor is configured to transmit a radio frequency signal, receive a composite signal including a plurality of backscatter signals. The plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment. The one or more objects include at least one moving object and one or more static objects. The at least one processor is further configured to detect the at least one moving object in accordance with the composite signal.

In one embodiment, an apparatus for tracing motion is disclosed. The apparatus includes, in part, one or more receivers, a memory and at least one processor coupled to the one or more receivers and the memory. The at least one processor is configured to receive information corresponding to one or more moving objects from one or more devices. The information comprises one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects. The at least one processor is further configured to estimate location of at least one of the one or more moving objects in accordance with the received information from the one or more devices.

In one embodiment, a non-transitory processor-readable medium for detecting motion in an environment is disclosed. The non-transitory processor-readable medium includes, in part, processor-readable instructions configured to cause one or more processors to transmit a radio frequency signal and receive a composite signal comprising a plurality of backscatter signals. The plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment. The one or more objects include at least one moving object and one or more static objects. The processor-readable instructions are further configured to cause the one or more processors to detect the at least one moving object in accordance with the composite signal.

In one embodiment, a non-transitory processor-readable medium for detecting motion in an environment is disclosed. The non-transitory processor-readable medium includes, in part, processor-readable instructions configured to cause one or more processors to receive information corresponding to one or more moving objects from one or more devices. The information includes one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects. The processor-readable instructions are further configured to cause the one or more processors to estimate location of at least one of the one or more moving objects in accordance with the received information from the one or more devices.

DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments accurately trace fine-grained motion using radio-frequency (RF) signals, such as WiFi signals. In one embodiment, a motion detection and/or tracing system can be integrated into WiFi transceivers. One embodiment detects and traces motion while WiFi transceivers are naturally transmitting packets for communication. Therefore, tracing functionality may be piggybacked on existing WiFi signals and use existing WiFi spectrum without compromising communication or throughput. One embodiment is capable of tracing independent motion of multiple objects. One embodiment is capable of detecting and tracing motion of one or more people that independently perform sets of fine-grained movements. Therefore, according to one embodiment, radio frequency transceivers, such as WiFi transceivers can be used as accurate, occlusion resistant, high resolution motion cameras capable of working in low light or dark conditions.

A motion tracing camera, according to one embodiment, uses radio frequency signals to detect motion. In one example, the radio frequency signals may act as a light source for the motion-tracing camera. The motion tracing camera is capable of accurately tracing a free-form motion of small objects such as human hands. The motion tracing camera as described herein can be used in gesture recognition applications, security applications, tracing human motion behind walls, or through smoke and fire, or any other applications. In addition, the motion tracing camera may be used in navigation systems for blind or elderly people, alerting them to other people/objects that might be coming from their side or their backs.

FIG. 1 illustrates an example home scenario, in which radio signals (e.g., WiFi signals) are scattered by objects, according to one embodiment of the present invention. In this example, a motion tracing device 102 including a WiFi radio (e.g., a Wi-Fi access point (AP)) is located close to a computer monitor 104 in a study room. In one example, hand movements of person A and person B who are located in the living room are traced. The living room is separated from the study room by a wall. There is a strong reflector (e.g., the computer monitor 104) close to the motion tracing device 102. The WiFi AP is being used for accessing the Internet, which is for example a 4-antenna 802.11ac AP. The rays in FIG. 1 show some of the backscatter reflections that arrive at the AP when it transmits a packet and the signals reflected from the objects in the home, including hands of person A and person B.

As illustrated in FIG. 1, the AP receives backscatter reflections from a nearby reflector (e.g., the monitor), as well as the faraway reflectors, such as hands of person B. Due to the difference in distance, as well as the fact that faraway backscatter signal has to reflect off or travel through walls, there is a considerable difference in signal strength between the two backscatter components. As described below, one embodiment provides a technique to capture and/or estimate weak backscatter signals. As illustrated in FIG. 1, the AP receives many backscatter reflections, out of which only a few correspond to reflections from the moving objects. The reason is the good propagation properties of WiFi frequencies and the abundance of reflectors in indoor environments. Many objects such as drywalls, tables, chair, laptops and floors reflect signals at the 2.4 GHz and 5 GHz frequencies. The challenge is to identify the backscatter signals that are reflected from the moving objects amongst these multitude of backscatter components.

FIG. 2 illustrates an example motion detection/tracing system, according to one embodiment. As illustrated, the motion detection/tracing system may include a server 202 and N transceivers 204 ₁ through 204 _(N). In general, the motion detection system may include one or more transceivers. In one example, four WiFi transceivers that are capable of tracing and tacking motion may be disposed in a room. Each of the transceivers may transmit a radio frequency signal and receive backscatter signals from objects in the environment. For example, one or more moving objects 206 and one or more static objects 208 may be located in the environment that reflect the radio frequency signals transmitted by the transceivers 204 ₁ through 204 _(N). According to one embodiment, each of the transceivers may process a composite signal that receives through its receive antenna(s). In general, each of the transceivers may include one or more transmit/receive antennas. The transceivers may estimate parameters corresponding to the moving object 206 and send the estimated parameters to server 202 for more processing. In one embodiment, server 202 isolates the backscatter generated by moving objects, and traces the motion after combining the measurements from all the transceivers. In general, each of the server and/or transceivers may be able to trace and track movement of the moving object 206.

FIG. 3 illustrates an example high-level block diagram of a motion detection and/or tracing system 300, according to one embodiment. As illustrated, the motion tracing system may include a backscatter measuring block 302, a backscatter isolating block 304, and a motion detecting block 306. The backscatter measuring block 302 measures each backscatter reflection of radio frequency transmissions individually. One embodiment enables the radio to isolate most of the reflections of its WiFi transmission from the environment. In addition, the radio measures/estimates attenuation and/or delay from time of transmission and/or angle of arrival (AoA) of each backscatter reflection. The backscatter isolating block 304 isolates backscatter signals that are reflected from moving objects from other backscatter signals that are reflected by stationary objects. For example, backscatter reflections may arise from different static objects such as walls, furniture, computers, and the like, as well as moving objects, such as people. The motion detecting block 306 uses a novel algorithm to detect and trace the actual motion that caused the specific backscatter reflections corresponding to the moving objects.

Several challenges need to be addressed in a motion tracing device using radio signals. For example, practical radio frequency transmitters usually have limited dynamic range and limited bandwidth. Dynamic range refers to the ratio of the power of the strongest to the weakest signal that can be received at the same by a radio. In a common scenario, a static reflector may be located close to the receiver and a moving reflector of interest may be located further from the receiver. Backscatter signals reflected from the nearby reflectors may be stronger than the backscatter signals reflected from far away moving objects by more than the dynamic range of the radio. Therefore, backscatter signals reflected from the nearby reflectors may drown the backscattered signals from the faraway moving objects and render them unmeasurable or difficult to measure. In addition, limited bandwidth means that the digital samples (e.g., output of the ADC) are spaced apart in time. For example, a 100 MHz ADC will produce samples that are 10 ns apart. RF signals travel around 10 feet in 10 ns, therefore, if there are two reflectors within 10 feet of each other, the receiver may not even be able to receive digital samples that are spaced within those reflections. In one embodiment, novel progressive backscatter cancellation techniques and compressed sensing-based estimation algorithms are used that accurately measure each backscatter component in spite of the limited dynamic range and bandwidth of practical transceivers.

A second challenge in designing a motion tracing device using radio signals is isolating the backscatters that are coming from the moving objects. In general, the backscatter from the moving reflector is subsumed within a sea of backscatter from the multitude of reflectors that exist indoors (walls, tables, laptops, etc.). To be able to detect and/or trace motion, one embodiment isolates the backscatter that corresponds to the moving reflector. A novel motion detection algorithm, according to one embodiment, exploits the fact that backscatter signal reflected from the static reflectors do not change over time and are highly correlated. By eliminating the backscatter signals that are highly correlated over time, the moving reflector's backscatter can be isolated.

It should be noted that the isolated backscatter signal from moving reflectors is often indirect. In other words, it is unlikely that the backscatter reflections from the moving object arrive straight back at the transmitting WiFi radio. Inevitably, due to the small surface area of these reflectors (e.g., hands) and varying angles, the backscatter further reflects off other objects (e.g., walls etc.) before it arrives at the radio. A novel motion tracing algorithm, according to one embodiment, starts with a hypothesis of determining what motion could have produced these sequence of indirect backscatter from the moving reflector, accounts for the bias that is introduced from multiple reflections and iteratively refines the actual motion hypothesis until it accurately is the observed backscatter, thus tracing the original motion. In one embodiment, the motion tracing algorithm uses a Kalman filter.

The present disclosure builds on recent work on measuring full duplex backscatter, application No. PCT/US2014/065814, entitled “Backscatter Estimation Using Progressive Self Interference Cancellation,” detail of which is incorporated by reference herein. While similar algorithms are used to measure backscatter, one embodiment further refines these algorithms. In addition, novel motion isolation and tracing algorithms are used to trace fine-grained motion.

One embodiment measures one or more parameters corresponding to the backscatter components (and specifically of the moving objects of interest), in addition to identifying and isolating these backscatter components. These parameters may include strength of backscatter signals, relative delay from the time the signal was transmitted to when the backscatter signal arrived at the AP, and the angle of arrival (AoA) at which the backscatter signal reaches the AP. Armed with this information about the backscatter signals that are reflected from the moving objects, one embodiment, traces the actual motion that produces the corresponding backscatter over time. Often the moving objects of interest (such as hands) are small and the backscatter reflections may not have a direct line of sight (LOS) path to the AP. So, the reflections from these objects further get reflected off of walls and other objects before arriving at the AP. In other words, the backscatter is indirect. The challenge is to discern the original motion that could have produced a measured pattern of indirect backscatter signals.

In addition, human motion is typically not a single moving object. For example, when someone's hand moves, his forearm and shoulder move in a coordinated fashion. Each of these limbs reflect signals independently. However, backscatter signals corresponding to these limbs are clearly borne out of a single human motion. Hence, according to one embodiment, the tracing algorithm discerns that all of the backscatter components are coming from a single coordinated human limb movement and declares the original motion. One embodiment is capable of tracing multiple human motions at the same time, whether the movement comes from a single person moving two limbs or different people moving their limbs. Therefore, the tracing algorithm as described herein is capable of detecting and tracing multiple movements that are occurring at the same time.

FIG. 4 illustrates example operations that may be performed by a motion detecting/tracing device, according to one embodiment. At 402, the motion detecting device transmits a radio frequency signal. At 404. The motion detecting device receives a composite signal. The composite signal includes a multitude of backscatter signals. The backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment. The one or more objects include at least one moving object and one or more static objects. At 406, the motion detecting device detects the at least one moving object in accordance with the composite signal.

FIG. 5 illustrates an exemplary block diagram of the motion detecting/tracing device, according to one embodiment of the present invention. FIG. 5 illustrates elements of the motion detecting/tracing device of FIG. 3 in more detail. The motion detecting/tracing device receives the backscatter signal, accurately estimates the components of the backscatter, isolates the components due to moving object, and nally traces the motion. The backscatter measuring block 302 may include a cancellation block 502, a composite channel estimation block 504, and a Sequential Convex Programming (SCP) block 506. The backscatter isolating block 504 may include a moving backscatter block 508 and a static backscatter block 510. Furthermore, the motion detecting block 306 may include a path association block 512, a Kalman tracking block 514, and a motion segmentation block 516. Each of these blocks are explained in detail below.

One embodiment provides accurate backscatter measurement even when using practical transceivers by designing two novel techniques to tackle the limitations of low dynamic range and finite bandwidth available in these transceivers. Assume that the radio frequency transceiver (e.g., WiFi radio) is transmitting a signal x(t). Also, there may be N backscatter reflections of the transmitted signal arriving back at the radio whose parameters are given by (α₁, τ₁, θ₁), . . . , (α_(n), τ_(n), θ_(n)) where α_(i) represents the attenuation undergone by the i^(th) backscatter component, τ_(i) represents the delay since the signal was transmitted, and θ_(i) represents the angle of arrival of the signal at the radio. For mathematical brevity, the angle of arrival (AoA) parameter is omitted in describing the basic problem in this section. Also, it is assumed that the radio has a single antenna. However, the problem statement can be easily extended to include the AoA parameter and multiple antennas, without departing from the teachings of the present disclosure. Hence, the overall signal that is arriving at the radio's antenna y(t) is given by:

$\begin{matrix} {{y(t)} = {\sum\limits_{i = 1}^{N}{\alpha_{i}{x\left( {t - \tau_{i}} \right)}}}} & (1) \end{matrix}$

In one embodiment, the reflected backscatter signals are received back at the radio. The RX chain of the radio is tuned to the same carrier frequency as the transmission. Bandwidth of the RX chain may be dictated by the sampling rate of the ADC (e.g., a 200 Msps ADC would imply trying to receive a 100 MHz signal). In one embodiment, RX chain of the radio down-converts the received signal to analog baseband, applies a low pass lter of bandwidth B corresponding to half the ADC sampling rate and then digitizes the signal. This process of ltering to a particular bandwidth B can be modeled as convolution with a filter of bandwidth B. The time domain response of a rectangular filter of bandwidth B is a sinc pulse. So, the overall signal received at baseband y_(B)(t) can be written as follows:

$\begin{matrix} {{y_{B}(t)} = {\left( {\sum\limits_{i = 1}^{N}{\alpha_{i}{x\left( {t - \tau_{i}} \right)}}} \right) \otimes {{sinc}\left( {B(t)} \right)}}} & (2) \end{matrix}$

In one embodiment, this baseband analog signal is sampled by the ADC to produce the discrete time signal y_(B)[n], as follows:

$\begin{matrix} {{y_{B}\lbrack n\rbrack} = {\left( {\sum\limits_{i = 1}^{N}{\alpha_{i}{x\left( {{nT}_{s} - \tau_{i}} \right)}}} \right) \otimes {{sinc}\left( {B\left( \; {nT}_{s} \right)} \right)}}} & (3) \end{matrix}$

where T_(s) is the sampling interval. The equation can be rewritten in terms of a sum of channel responses of each backscatter reflector as follows:

$\begin{matrix} {{y_{B}\lbrack n\rbrack} = {\left( {\sum\limits_{i = 1}^{N}{\alpha_{i}{{sinc}\left( {B\left( {{nT}_{s} - \tau_{i}} \right)} \right)}}} \right) \otimes {x\lbrack n\rbrack}}} & (4) \end{matrix}$

The term in the parenthesis can be viewed as the total system through which the transmitted signal is passing. This includes the backscatter channel, which can be viewed as sum of impulses, and the lters at the transmit/receive chain which can be viewed as a channel with impulse response sinc(B(t)). Hence, the total channel through which the transmitted signal passes is sum of appropriately weighted and shifted sinc signals, as illustrated in FIGS. 6A-6C.

FIGS. 6A-6C illustrate example backscatter channel according to one embodiment of the present invention. In the example illustrated in FIGS. 6A and 6B, the backscatter channel is composed of three impulses. Parameters (α₁, τ₁, θ₁), . . . , (α₃, τ₃, θ₃) corresponding to each of the backscatter impulses should be estimated. However, only the combined channel response, as illustrated in FIG. 6C is available. In the above equation, the device knows the transmitted signal x[n] and the received signal y_(B)[n]. From these two variables, the overall channel response h[n] can be calculated using classic channel estimation techniques based on preambles and pilots. The overall channel response is related to the individual backscatter components, as follows:

$\begin{matrix} {{h\lbrack n\rbrack} = \left( {\sum\limits_{i = 1}^{N}{\alpha_{i}{{sinc}\left( {B\left( {{nT}_{s} - \tau_{i}} \right)} \right)}}} \right)} & (5) \end{matrix}$

The above equation summarizes the backscatter measurement problem. Individual backscatter components should be estimated, even though only the overall response h[n] is known. This is an extremely under-constrained problem, since there are many more unknowns than equations. In one embodiment, the problem in Eqn (5) is casted as a sparse optimization problem based on the physical intuition that in any environment there are typically only a few reflectors (on the order of tens) rather than hundreds. Hence, the number of backscatter components arriving at the AP at any point is a relatively small number. In one embodiment, adding this additional constraint of finding the sparsest number of backscatter components that can produce the overall response, leads to an optimization problem, as follows:

minimize Σ_(n)(h[n]−Σ _(k)α_(k)sinc(B(nT _(s) −τk)))²+λ_(r)|α|₀

subject to τ_(k)≧0, α_(k)≦1,

k={1, . . . , L}, n={−N, . . . , N},  (6)

The above optimization problem is non-linear because the sinc terms in the optimization problem are non-linear. Further, the problem is not convex. One embodiment solves this channel estimation problem using a piece-wise technique. In one example, the mon-linear optimization problem may be solved in the composite channel estimation block 505, as illustrated in FIG. 5. For solving the non-linear optimization problem, first, a small region in the variable space δτ, δα, δθ is focused on. The sinc functions are approximated in that region by straight lines, and the resulting convex optimization problem is solved. Next, the residual error is compared and the process is repeated until successive iterations of the algorithm does not reduce the error any further. Solving the above general problem however leads to two challenges stemming from the limitations of radio hardware, which will be discussed next.

In general, some of the reflections in the backscatter may be significantly stronger than others. For example, if there is a reflector 10 cm away from the transmitter and another 10 m away, the difference in signal strengths of these two components can be as high as 60 dB. Practical WiFi transceivers have a typical dynamic range of 60 dB, which would imply that the weaker reflection would get completely buried. Second, even if the weaker reflection is within the dynamic range, it is not being represented with many bits and hence there might be higher quantization noise. While the strong components can be estimated reasonably accurately, the weaker components may not be and inevitably this leads to inaccurate backscatter parameter estimation. One embodiment improves accuracy of the estimation by progressively removing the strongest backscatter component and allowing the estimation algorithm to operate on the remaining components, as shown in FIGS. 7A through 7D.

FIGS. 7A through 7D sequentially illustrate intermediate results of an estimation of the backscatter signal after performing the progressive self-interference cancellation according to one embodiment of the present invention. As illustrated, by removing the stronger components as illustrated in FIG. 7A, dynamic range of the receiver can be used to sample the weaker components with higher resolution and thus prevent them from being washed out or distorted (as shown in FIG. 7B). When components are estimated, the strongest of the remaining components is removed, and the estimation algorithm is run on the remaining backscatter signals. The algorithm recursively repeats until all components are estimated (as illustrated in FIGS. 7C and 7D).

One embodiment progressively cancels strong backscatter components and improves the accuracy of estimation for the remaining components. The challenge therefore is to selectively eliminate backscatter components by canceling them. In one embodiment, the cancellation is implemented in analog before the receiver, since otherwise dynamic range will be limited. To implement this, the authors build on prior work in self-interference cancellation and full duplex, as filed in Application No. 61/864,492, entitled “Full Duplex Radio,” detail of which is incorporated herein in its entirety. The analog cancellation circuit design in this prior work is modified to cancel the backscatter components (e.g., interference) in a controlled manner one by one. One embodiment uses the initial estimates to find a coarse estimate of where the strong backscatter components are located. The analog cancellation circuit is then tuned to only cancel these components by controlling the delay taps in the circuit to straddle the delay of these strong components. After running the estimation algorithm on the remaining backscatter signals, the analog cancellation circuit is retuned to cancel the next strongest component and repeat the process. In the rest of the present disclosure, this cancellation technique is referred to as Progressive self-Interference Cancellation (PIC).

In one embodiment, after PIC cancellation, the remaining backscatter signal components can be estimated. Further, similar cancelling and estimation procedures can be performed in digital domain by selectively canceling the residual backscatter components using the digital cancellation algorithms.

In general, closely spaced reflections of the transmitted signal may lead to a degenerate system of equations. For example, when the reflections at two delays τ₁ and τr₂ are closely spaced, the difference in sample values of the corresponding sinc functions are very small and therefore the observations are highly correlated. As more reflections are closely spaced within a sampling period, the problem of uniquely reconstituting the reflections becomes more difficult. Consequently, reconstruction error increases since the optimization algorithm struggles to find a good fit. In one embodiment, even if there were two very closely spaced reflectors in time (i.e., the relative delays from the transmitter to the two reflectors were within a sampling period of each other), they could still be deconstructed accurately as long as their spatial orientations relative to the transmitter are slightly different (i.e., their backscatter signals have different AoAs).

The basic idea can be visualized as follows. In one example, a radio has four antennas and the two closely spaced reflections are arriving at angles θ₁ and θ₂ respectively. The two reflections can be distinguished in the spatial dimension because they exhibit different phases at the different antennas. The reason is that because of the different angles of arrival, the two reflections travel different distances which translates to a phase difference across successive antennas. In one embodiment, by incorporating this constraint into the optimization problem, closely-spaced reflections can be disentangled within a single sampling period. In one embodiment, up to 4 closely spaced reflections can be distinguished within a single sampling period. In effect, one embodiment achieves the same performance as an ADC that has four times the sampling bandwidth of its radio, while still using inexpensive commodity transceivers.

Formal Mathematical Model

In this section a brief description of the formal mathematical model, according to one embodiment is provided. The composite backscatter channel as seen by the m^(th) receiver in a MIMO antenna array can be modeled as follows:

$\begin{matrix} {{h_{m}\lbrack n\rbrack} = {\sum\limits_{k}{\alpha_{k}^{{({v_{k} + {\gamma \; {mk}}})}}{{sinc}\left( {B\left( {{nT}_{s} - \left( {\tau_{k} + \frac{\gamma \; {mk}}{2\pi \; {fc}}} \right)} \right)} \right)}}}} & (7) \end{matrix}$

where

$\gamma_{mk} = {\frac{2\pi}{\lambda}\left( {m - 1} \right)d\; \sin \; \theta_{k}}$

is the added phase shift experienced by the k^(th) reflection at the mth receiver relative to the first receiver when the reflected signal arrives back at θ_(k) AoA, α_(ke) ^(ivk) is the complex attenuation for the k^(th) reflection, and τ_(k) is its corresponding delay. The constant f_(c) is the carrier frequency with wavelength of λ, and d is the distance between the successive receiving antennas in the array. Theoretically, h_(m)[n] can be of infinite length, but in practice the sinc function decays to very small values for large values of n. Thus, the channel can be modeled by a finite-length vector with n having value in the range of [−N, N].

In one embodiment, the composite nite-length linear channel h_(m)[n] can be estimated by de-convolving the received samples with known preamble samples. Once the composite linear channel has been estimated, the parameters of the constituent backscatter components can be estimated by solving the following optimization problem:

$\begin{matrix} {{{{minimize}{\sum\limits_{m}{\sum\limits_{n}{{{h_{m}\lbrack n\rbrack} - {{\overset{\_}{h}}_{m}\lbrack n\rbrack}}}^{2}}}} + {\lambda_{r}{\alpha }_{1}}}{{{{subject}\mspace{14mu} {to}\mspace{14mu} \tau_{k}} \geq 0},{\alpha_{k} \leq 1},{\theta_{k} \in \left\lbrack {\frac{- \pi}{2},\frac{\pi}{2}} \right\rbrack},{v_{k} \in \left\lbrack {0,{2\pi}} \right\rbrack},{k = \left\{ {1,\ldots \;,L} \right\}},{n = \left\{ {{- N},\ldots \;,N} \right\}},{m = \left\{ {1,\ldots \;,M} \right\}}}} & (8) \end{matrix}$

where {tilde over (h)} is the estimated linear channel response obtained by the de-convolution of received samples with a known preamble, M is the number of antennas in the array, and L is the total number of reflected backscatter components, λ_(r) is the regularization coefficient chosen to be a positive number. In one embodiment, the combinatorial objective |α|₀ due to l₀ norm in equation 6 is replaced with the convex relaxation |α|₁ using l₁ norm in the above formulation.

This optimization problem is non-convex and is not known to have a global solution. Instead, in one embodiment, this problem is solved approximately by finding a locally optimal solution. In one embodiment, a heuristic known as Sequential Convex Programming (SCP) is used in the SCP block 506 as illustrated in FIG. 5. In the SCP algorithm, all the non-convex functions are replaced by their convex approximation. The resulting convex problem is then solved in an iterative manner until a reasonable solution is achieved. One simple approach to convert Eqn. (8) into a convex problem, according to one embodiment, is to approximate Eqn. (7) by a linear function. It should be noted that a more sophisticated convex-approximation of Eqn. (7), or any other method could also be used to improve overall accuracy of estimation without departing from the teachings of the present disclosure.

Due to a limited dynamic range of the ADC in typical receivers, stronger backscatter components may mask the weaker components. In one embodiment, in the initial step, the algorithm finds the parameters corresponding to the strong backscatter components and then passes that information to the progressive self-interference cancellation (PIC) block. The PIC block will cancel these reflections, which allow the receiver AGC to increase gain for the weak backscatter components which can now become detectable due to the cancellation of the strong reflections. In one embodiment, the estimation algorithm is run again to estimate the parameters of the weak components and send back that information to PIC for further cancellation.

In practice, the backscatter components are weaker for larger values of delay τ. Hence, in one embodiment, the estimation algorithm can be run by partitioning the range for the variable τ into several non-overlapping windows. In one embodiment, the algorithm begins by estimating parameters for the reflections in the first time window then passes that information to the PIC. After cancellation of these components, advance the window one step into the future and then estimate the parameters in that range and pass that information to PIC for cancellation. This process can repeat multiple times until all the backscatter components are detected in the forward time direction. In one embodiment, a reasonably high value can be set for τ beyond which the remaining backscatter components are very weak. This maximum value may depend on the application and the environment. The estimation process may be described in the Algorithm 1, as follows:

Algorithm 1: Progressive steps in backscatter channel parameters estimation 1. Begin in the first time window w₀ 2. Estimate parameters α, τ, θ for reflectors in window w₀ by solving the optimization problem given by Eqn. 8 3. repeat 4. Pass the estimated parameters of all reflectors in the past windows w_(p−1...) w₀ to analog PIC block for cancellation 5. Estimate parameters α, τ, θ for reflectors in the current window w_(p) by solving the optimization problem given by Eqn. 8 6. Advance window to w_(p+1) 7. until Parameters for reflectors in the last window w_(P) has been estimated by forward estimation

Isolating Backscatter from Moving Objects

Measuring and estimating backscatter via the algorithm described in the previous section generates a set of parameters (amplitude, delay, AoA) for each backscatter component that is reflecting and arriving back at the transceiver. The majority of these reflections are from static objects that are not moving and hence act as noise to the motion tracing algorithm. Referring back to FIG. 5, the backscatter isolating block 810 identifies and eliminates this static background by isolating the static backscatters into the static backscatter block 810 and feeding information corresponding to the static objects into the cancellation block 502 to remove them from the composite received signal. The backscatter isolating block 304, further isolates the backscatter components that are generated by moving objects into the moving backscatter block 508.

One embodiment models the backscatter components and their corresponding parameters as image frames. At each time step, the motion detecting device forms a three dimensional image frame (with the three backscatter parameters such as delay, AoA and amplitude as the axes), where each pixel in the frame corresponds to one backscatter component arriving at the AP. As multiple measurements are made by the motion detecting device over time, a sequence of frames is generated, as illustrated in FIGS. 8A through 8C.

FIGS. 8A through 8C illustrate example backscatter components obtained from a simulated hand movement in a typical indoor scenario using a Wireless tracing software, according to one embodiment of the present invention. In this figure, the backscatter components collected in each time interval are presented as image frames. The horizontal and vertical axes correspond to delay and AoA, respectively. Different brightness of each pixel corresponds to its amplitude (power in dBm). Therefore, each colored pixel corresponds to a backscatter component. Different frames stacked one over the other correspond to set of backscatter components obtained in consecutive time intervals. The majority of backscatter components are contributed by static environment, which are shown in the same color to provide contrast with moving backscatter.

In one embodiment, one frame may be collected after every predefined amount of time, which can be tuned. In one example, one frame is collected every 1 ms, which results in one thousand frames per second. The backscatter components of each frame are collected over data that is collected every millisecond assuming the transceiver is transmitting for at least some fraction of that millisecond. If the transceiver is not transmitting, then there is no backscatter to collect, in one embodiment, for the period in which transmitter does not transmit signals, an empty frame may be considered.

In one embodiment, by modeling backscatter as image frames, the problem of isolating backscatters can be modeled using video motion estimation techniques. In one embodiment, pixels that correspond to the same backscatter component (i.e., generated by the same backscatter reflector) are associated between every two successive frames. The association is performed even if the reflector moved between those two frames. To do so, according to one embodiment, a novel pixel association algorithm is used across frames based on minimizing the amount of change between consecutive frames.

In one embodiment, the pixel association algorithm starts by calculating a pairwise distance between every two pair of pixels in successive frames. Distance is defined as the absolute difference in the three parameters (amplitude, delay and AoA) squared and summed after appropriate normalization, as follows:

d(b ₁ ,b ₂)=−w_(α)(α₁−α₂)² +w _(τ)(τ₁−τ₂)² +w _(θ)(θ₁−θ₂)²  (8)

where b₁ and b₂ are the two backscatter pixels in successive frames and α, τ, and θ are the amplitude, delay and AoA respectively. The weights w_(α), w_(τ), and w_(θ) are inverse of the largest squared distance between pairwise amplitude, delay, and AoA, respectively, of the pixels in the two frames. Note that this metric can be calculated for all pairs of pixels, so there would be n² distances where n is the number of pixels in a frame. In one embodiment, specific pairings may be found between frames, where pixels in each pair of frames are generated by the same backscatter reflector.

It should be noted that for static objects, the pixels corresponding to backscatter reflections from that static object in successive frames should be at zero distance with respect to each other because by definition they did not move and the associated parameters did not change. Further, even for the pixels that correspond to motion, given how slow human motion is relative to the length of a frame (a millisecond), the distance between pixels in successive frames that correspond to the moving object could be small. In one embodiment, the pixels are paired up such that the overall sum of the distance metrics for these paired pixels is the minimum among all possible pairings.

Isolating Moving and Static Backscatter

Determining the right pixel association between successive frames may be modeled as a combinatorial assignment problem. Distances between all pairs of pixels are passed to the system as the input and a set of pairs that minimize the overall sum of distance metric among the pixels are selected. A naive algorithm would be to enumerate all possible assignment of pixel pairs, which would require evaluation of n! assignments for frames with n pixels. In one embodiment, a classic algorithm in combinatorial optimization known as the Hungarian algorithm is used to solve the pixel association problem. However, any other method may be used to find pixel pairs without departing from the teachings of the present disclosure. The Hungarian algorithm runs in polynomial time (O(n³), where n is the number of pixels.

A full description of the Hungarian algorithm is omitted for brevity, however, the Hungarian algorithm is best visualized in terms of a bipartite graph G=(F1, F2, E). In the bipartite graph, pixels from the rst frame are vertices in the set (F1) and pixels from the second frame are in the set (F2) and the edge set (E) consists of all possible edges between vertices in the two sets. The weight of each edge is the distance metric between the backscatter parameters corresponding to the pixels the edge connects. In one embodiment, a preferred matching with minimum cost is determined, as illustrated in FIG. 9B.

FIGS. 9A and 9B illustrate an example application of Hungarian algorithm for a subset of backscatter components, according to one embodiment of the present invention. FIG. 9A represents the backscatter components in two successive frames. The color of each pixel is a representation of the value of α (power in dBm), θ (in degrees), or τ (in ns) according to the appropriate row. A distance metric is defined between each component in the top frame and each component in the bottom frame. The distances that are obtained are represented as edges with appropriate weights. The matching with minimum weight may be found in the bipartite graph. Applying Hungarian algorithm results in the least weight matching presented in FIG. 9B, thus providing a way to associate backscatter components in the top frame with corresponding components in bottom frames.

In one embodiment, the set of associated pixels across frames can be used to identify the backscatter reflectors that are static, and the backscatter reflectors that are moving. For example, the backscatter components are separated into background components and foreground components as illustrated in FIGS. 8A-8C. In other words, the moving reflectors are identified and isolated to focus on tracing their motion. In addition, the static background objects that act as noise may be eliminated from consideration. In one embodiment, pairs of pixels whose distance metric is close to zero and remains close to zero for at least a predefined number of frames are identified. For example, if the distance metric is close to zero for about 100 frames, it corresponds to the reflectors that have been static for 100 ms. In one embodiment, when such pixels are found, they are declared to be part of the static background. These pixels may then be eliminated from the frames. In one embodiment, when the static pixels are identified and removed from frames, the only pixels left are those that the algorithm believes to be coming from moving reflectors.

The information about these static pixels is also passed to the backscatter measurement 302 and progressive interference cancellation block 502, described above. The measurement block uses this information to permanently cancel these components using progressive interference cancellation. Further updates from the backscatter measurement block 302 do not have information about these pixels in the frames, thus making detecting backscatter components corresponding to moving objects easier.

The static elimination step also naturally provides detection of a new movement that is starting. For example, a completely static environment can be assumed at first. In this example, in the steady state the backscatter measurement block wont report any parameters because eventually all of the components will be declared static and canceled by progressive interference cancellation. When a new motion starts and generates new backscatter components, the measurement block will report these parameters to the backscatter isolation block 304 which will classify them as moving pixels. Such pixels are grouped together and passed to the motion detecting block 306 as a new moving object that needs to be traced.

At this point, a set of frames are identified with pixels that correspond to moving objects. Further, pixels in successive frames are associated with each other via the Hungarian algorithm if they belong to the same backscatter reflector. The next step is to trace the original object and its motion which produced the frames with the moving pixels.

Referring back to FIG. 2, in one embodiment, one or more of the transceivers 204 ₁ through 204 _(N) send information corresponding to their detected frames to the server. For example, transceiver 204 ₁ sends the frames that was computed in the previous step to the server. It should be noted that the server may not be physically separate from the transceivers. For example, in one embodiment, the server may be located in the same device as one of the transceivers. In general, each of the transceivers may have functionality to act as the server, without departing from the teachings of the present disclosure.

Whenever a new motion starts, it may be quite likely one or more transceivers in a neighborhood declare a frame with some moving backscatter. The server collects frames over a predefined period of (e.g., 100 ms) from all participating radio transceivers, and assumes that any moving backscatter detected by any of the transceivers are coming from the same object. In one embodiment, the heuristic implicitly makes the assumption that two new and independent motions won't start within an interval of, for example, 100 ms. In one example, considering human motion and the timescales at which human motion happens, 100 ms is a negligible amount of time and such asynchrony is very likely in practice. Note that this does not mean that two independent motions cannot be occurring simultaneously, one embodiment only makes the assumption that these motions don't start within 100 ms of each other.

The key challenge in tracing motion is that each moving object is likely to produce indirect backscatter because of their small sizes. After the signal reflects off a moving object, it may be quite likely that it reflects off another object (such as a wall) before arriving back at the transmitting radio. Hence, most of the backscatter measurements for a component do not correspond to a LOS path from the radio to the moving object.

Localizing the Centroid of the Motion

In one embodiment, the server 202 localizes the centroid of the motion that has just started. The server may receive measurements from one or more transceivers 204 ₁ through 204 _(N) across multiple frames. In one example, if there are N transceivers collecting measurements, the server picks N frames that were collected almost at the same time by the N transceivers. Each one of them has a backscatter component and the associated parameters (α, τ, θ): amplitude, delay and AoA). The goal is to estimate location (x, y) of the moving object at a specific time.

Since backscatter at each radio can be indirect, the backscatter measurements have a constant bias in them. For example, if a backscatter reflection from a moving object is further reflected by a wall before arriving at the AP, the delay parameter will have a constant bias that reflects the extra time it takes to traverse the extra distance corresponding to going to the wall and reflecting off the wall. Similar bias exists for both the amplitude and AoA measurements. It should be noted that the bias values are unknown.

Given the above, in one embodiment, the server solves the following localization problem:

$\begin{matrix} {{{minimize}\mspace{14mu} {\sum\limits_{j = 1}^{N}{\sum\limits_{i = 1}^{M}\left\lbrack {\left( {{\overset{\_}{p}}_{i} - p_{ij}} \right)^{2} + \left( {{\overset{\_}{\tau}}_{i} + b_{\tau_{i}} - \tau_{ij}} \right)^{2} + \left( {\theta_{i} + b_{\theta_{i}} - \theta_{ij}} \right)^{2}} \right\rbrack}}}\mspace{85mu} {{{\overset{\_}{\tau}}_{i} = \frac{{dist}\left( {x,x_{i)}} \right.}{x}}\;,{b_{\tau_{i}} \geq 0}}\mspace{79mu} {{\overset{\_}{\theta}}_{i} = {{AoAULA}\left( {x,x_{i}} \right)}}\mspace{79mu} {{{subject}\mspace{14mu} {to}\mspace{14mu} {\overset{\_}{p}}_{i =}p_{0i}} - {10\; \gamma_{i}{\log_{10}\left( {{dist}\left( {x,x_{i}} \right)} \right)}}}\mspace{79mu} {{{\gamma \; i} \geq 0},{i = \left\{ {1,\ldots \;,M} \right\}},{j = \left\{ {1,{\ldots \; N}} \right\}}}} & 10 \end{matrix}$

where M is the number of tracing transceivers, N is the number of backscatter frames that is being used for the localization, p_(ij), τ_(ij), and θ_(ij) are the received power, delay, and the AoA respectively of the backscatter in the jth frame observed by the ith transceiver. The dist(x, x_(i)) is the Euclidean distance function between two points x and x_(i), and AoAULA(x, x_(i)) is a function that gives the AoA seen by the tracing transceiver located at x_(i) from a reflector located at x. The distance is converted to delay using speed of light c=3×10⁸ m/s. p_(0i) is the reference power reflected by a known reflector located at a distance of 1 m, and λ_(i) is the path loss coefficient of the environment. b_(ti) and b_(θi) are the bias added in the delay and AoA respectively due to multipath. In one embodiment, it can be assumed the bias values b_(ti) and b_(θi) remain the same for few frames as most of the moving objects (such as humans) are unlikely to move very far in a short timeframe. The basic intuition is to find the best fit for a location x that minimizes the above metric. Since the parameters p_(0i), γ_(i), b_(τi) and b_(θi) are also not known, they will be estimated along with the unknown location x.

In one embodiment, the optimization problem can use the data from several frames (i.e., a few milliseconds) given that a moving object (e.g., human) isn't likely to have changed location significantly in a few milliseconds. In one embodiment, the optimization problem provides a location, as well as an estimate for the bias of each parameter at each measuring transceiver. This bias estimation will be useful later in tracing motion. The above optimization problem is non-convex, therefore , in one embodiment, a heuristic known as sequential convex optimization may be used to solve it.

In one embodiment, it can be assumed that the location of the transceivers x_(i) (e.g., motion tracing devices) that are measuring backscatter are known. To make this assumption, it can be assumed that there are at least a few transceivers (such as the APs) whose location is known a priori, and any other transceiver can then localize itself using standard localization algorithms.

Tracing Motion

Once the newly detected moving object is localized, the next step is to trace the motion as it moves. One embodiment builds a hypothesis about the motions that are occurring and progressively reflnes it. There are several parameters to that hypothesis such as initial position, velocity, direction of motion and bias in each backscatter parameter due to indirect reflections, and any other parameters. In one embodiment, both the bias and initial position variables are initialized using the output of the localization algorithm in the previous step. Velocity and direction of motion are then updated as new measurements become available. Note that the bias parameter may change over time, because when the object moves the bias for each parameter changes.

One embodiment uses a classic algorithm from estimation theory (e.g., Kalman filter) for estimating these parameters and updating them in block 514, as shown in FIG. 5. The Kalman filter works as follows: at every step compares its prediction of where the pixel should have been given its estimates of velocity, direction and bias, and where it actually is in the frame of each measuring radio. Kalman filter uses the error to update its estimates. In one embodiment, the motion is represented by the following dynamic model:

$\begin{matrix} {{{x_{k + 1} = {{A_{k}x_{k}} + w_{k}}},{w_{k} \sim {N\left( {0,Q_{k}} \right)}}}{{y_{k} = {{C\left( x_{k} \right)} + v_{k}}},{v_{k} \sim {N\left( {0,{{R_{k)}A_{k}} = \left\lbrack \frac{A}{0} \middle| \frac{0}{I} \right\rbrack},{A = \begin{bmatrix} 1 & 0 & {\delta \; T} & 0 \\ 0 & 1 & 0 & {\delta \; T} \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix}},,} \right.}}}} & (11) \end{matrix}$

where x_(k) is the state vector at frame k which consists of terms x_(k)=[x_(k) y_(k) {dot over (x)}_(k) {dot over (y)}_(k) b_(θ) _(k) b_(τ) _(k) p_(0k) γ_(k)]^(T), where x_(k) and y_(k) are the x and the y coordinate; and {dot over (x)}_(k) and {dot over (y)}_(k) represent the velocity in x and y direction of the human; and b_(θ) _(k) , b_(τ) _(k) , p_(0k), and γ_(k) are the bias in AoA, bias in delay, the reference power and the path loss coefficient respectively. The matrix A_(k) helps to update the state between frames, where 0 is the zero matrix; I is the identity matrix and δT is the time interval between the frames; w_(k) is the random variable representing the modeling error with Q_(k) as its co-variance matrix; y_(k) is the observation that consists of the measurement quantities power, delay, and AoA.

It should be noted that these observations are not linear function of the state variable x_(k). Therefore, in one embodiment, extended Kalman filter is used to trace the motion. v_(k) is the random variable indicating the measurement errors with co-variance matrix R_(k). Additionally, Kalman filter is also used to associate backscatter parameters pertaining to moving objects between the frames. The major update steps of the extended Kalman filter is given in Algorithm 2, as follows:

1. Begin Initialize Kalman filter using the location and bias computed by the localization algorithm 2. repeat 3.  Kalman propagation {circumflex over (x)}_(k) ⁻ = A_(k−1){circumflex over (x)}_(k−1) ⁻,  P_(k) ⁻ = A_(k−1)P_(k−1) ⁺A_(k−1) ^(T) + Q_(k−1) 4.  Linearize C: Linearize and non-linear function C around the previous state   ${{variable}\mspace{14mu} {\hat{x}}_{k}^{-}\mspace{14mu} {and}\mspace{14mu} {compute}{\mspace{11mu} \;}{the}\mspace{14mu} {gradient}\mspace{14mu} {matrix}\mspace{14mu} {H_{k}\left( {\hat{x}}_{k}^{-} \right)}} = \left. \frac{\partial C}{\partial x} \right|_{x = {\hat{x}}_{k}^{-}}$ 5.  Kalman gain K_(k) = P_(k) ⁻¹H_(k) ^(T)({circumflex over (x)} _(k) )[H_(k)({circumflex over (x)} _(k) )P_(k) ⁻H_(k) ^(T)({circumflex over (x)} _(k) ) + R_(k)]⁻¹ 6.  Parameter association: Using the propagated state computed the predicted  values of the measurement and apply Hungarian algorithm to find the likely  observations in the new frame 7.  Kalman update: Update the propagated state and the co-variance matrix as  {circumflex over (X)}_(k) ⁺ = {circumflex over (X)}_(k) ⁻ + K_(k)[_(yk) − C({circumflex over (X)}_(k) ⁻)], P_(k) ⁺ = [I − K_(k)H_(k) ^(T)({circumflex over (X)}_(k) ⁻)]P_(k) ⁻ 8. until The association fails to find a valid parameters for few of the new consecutive frames

In one embodiment, convergence of the filter takes a few frames (on the order of tens of milliseconds). After this point, the filter constantly updates its estimates of velocity and direction of motion. Reconstructing the motion is now akin to starting with the initial point and performing a directional piece-wise integration using the velocity and direction of motion parameter at each time step as described in Algorithm 2.

As explained before, some motions, such as a human motion, cannot be considered a single object moving. For example, when a hand moves it involves the movement of the forearm and the shoulder in a coordinated fashion. Each of these body parts could be producing their own backscatter components and the above algorithm will trace two or more different motion paths for them. One embodiment determines whether or not different motion paths arose from a single object. It is noted that if there are set of connected motions as described above, there is a clear geometrical relationship between them. The intuition is that the shape of the motion traces for these connected objects is very similar, they might just be shifted and scaled as shown in FIGS. 10A and 10B. The precise mathematical relationship between these traces is that every point on the trace is a scaled and shifted version of the corresponding point in the other trace. Therefore, corresponding points in the two traces have an affine relationship.

FIGS. 10A and 10B illustrate an example movement of a person's hand 1002, according to one embodiment of the present invention. As illustrated, when hand 1002 moves, multiple parts of the hand move in a coordinated fashion. For example, in FIG. 10A, the elbow point 1004 performs a similar trajectory to that performed by hand 1002. However, it is a scaled and shifted version of the trajectory performed by hand, and the same is reflected in the tracing by the Kalman filter, illustrated in FIG. 10B. Moreover, there may be multiple copies of the signal received at the system due to indirect reflections, which also result in translated versions of the trajectory of hand.

In one embodiment, a spectral clustering algorithm called sub-space clustering algorithm (SSC) is used in motion segmentation block 516, as illustrated in FIG. 5, to combine motions that are affinity related to each other. The idea is to cluster all motion traces together which exhibit an affine relationship between them. The basic metric in adding a trace to a cluster is whether or not the trace can be written as a linear combination of other traces already in the cluster plus a constant. The algorithms automatically finds the number of clusters to use. An outcome of this step is a set of clusters and motion traces in each cluster. The number of clusters represents the number of moving objects in the environment. The traces in each cluster are normalized to show the overall motion trace for each object in the environment. As illustrated in FIG. 10B, there are two different clusters 1006 and 1008, each corresponding to a different moving object. For example, cluster 1006 corresponds to the movement of the hand 1002 and elbow 1004 as illustrated in FIG. 10A.

FIG. 11 illustrates example operations that may be performed by a device to detect motion, in accordance with one embodiment of the present disclosure. In one embodiment, the device is a radio frequency transceiver. At 1102, the device estimates one or more parameters corresponding to a plurality of backscatter signals corresponding to at least one moving object. The one or more parameters are estimated by progressively removing one or more backscatter signals corresponding to the one or more static objects from the composite signal. The device may estimate the one or more parameters for a plurality of time stamps during a first time period. At 1104, the device generates a plurality of image frames corresponding to one of the plurality of time stamps. Each image frame has a plurality of pixels, each pixel corresponding to one of the plurality of backscatter signals. At 1106, the device identifies a plurality of associations between the plurality of pixels in the plurality of image frames. At 1108, the device identifies a plurality of paths corresponding to the at least one moving object in accordance with the plurality of associations. At 1110, the device determines one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object. The device may then trace motion of the detected moving object.

FIG. 12 illustrates example operations that may be performed by a device to trace motion, in accordance with one embodiment of the present disclosure. In general, the device may be a stand-alone server or a transceiver capable of processing information. At 1202, the device receives information corresponding to one or more moving objects from one or more devices (e.g., transceivers). The information includes one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects. At 1204, the device estimates location of at least one of the one or more moving objects in accordance with the received information from the one or more devices. At 1206, the device identifies a plurality of paths corresponding to the at least one moving object in accordance with the received information. At 1208, the device determines one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object. At 1210, the device determines a number of moving objects corresponding to the number of motion categories. At 1212, the device traces motion of the at least one moving object in accordance with the identified plurality of paths and/or the motion categories.

FIG. 13 is a simplified block diagram of an exemplary computer or data processing system 1300 in which portions of the motion tracing/detection device, may be embodied. Computer system 1300 is shown as including a monitor 1310, a computer 1320, user output devices 1330, user input devices 1340, communications interface 1350, and the like.

As shown in FIG. 13, computer 1320 may include one or more processors or processing units 1360 that communicates with a number of peripheral devices via a bus subsystem 1390. These peripheral devices may include user output devices 1330, user input devices 1340, communications interface 1350, and a storage subsystem, such as random access memory (RAM) 1370 and non-volatile memory 1380.

User input devices 1330 include all possible types of devices and mechanisms for inputting information to computer system 1320. These may include a keyboard, a keypad, a touch screen incorporated into the display, audio input devices such as voice recognition systems, microphones, and other types of input devices. User input devices 1330 typically allow a user to select objects, icons, text and the like that appear on the monitor 1310 via a command such as a click of a button or the like. User output devices 1340 include all possible types of devices and mechanisms for outputting information from computer 1320. These may include a display (e.g., monitor 1310), non-visual displays such as audio output devices, etc.

Communications interface 1350 provides an interface to other communication networks and devices. Communications interface 1350 may serve as an interface for receiving data from and transmitting data to other systems. For example, the communication interface 1350 may transmit radio frequency signals and/or information corresponding to the backscatter signals, and receive composite receive signal and/or other information. In various embodiments, computer system 1300 may also include software that enables communications over a network.

RAM 1370 and disk drive 1380 are examples of tangible media configured to store data including, for example, executable computer code, human readable code, or the like. Other types of tangible media include floppy disks, removable hard disks, semiconductor memories such as flash memories, non-transitory read-only-memories (ROMS), battery-backed volatile memories, and the like. RAM 1370 and non-volatile memory 1380 may be configured to store the basic programming and data constructs that provide the functionality described above in accordance with embodiments of the present invention. For example, the memory stores instruction o for detecting and tracing motion, according to one embodiment. Software code modules and instructions that provide such functionality may be stored in RAM 1370 and/or non-volatile memory 1380. These software modules may be executed by processor(s) 1360. RAM 1370 and non-volatile memory 1380 may also provide a repository for storing data used in accordance with embodiments of the present invention.

RAM 1370 and non-volatile memory 1380 may include a number of memories including a main random access memory (RAM) for storage of instructions and data during program execution and a read only memory (ROM) in which fixed non-transitory instructions are stored. RAM 1370 and non-volatile memory 1380 may include a file storage subsystem providing persistent (non-volatile) storage for program and data files. RAM 1370 and non-volatile memory 1380 may also include removable storage systems, such as removable flash memory.

Bus subsystem 1390 provides a mechanism for enabling the various components and subsystems of computer 1320 communicate with each other as intended. Although bus subsystem 1390 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple busses.

Various embodiments of the present invention may be implemented in the form of logic in software or hardware or a combination of both. The logic may be stored in a computer readable or machine-readable non-transitory storage medium as a set of instructions adapted to direct a processor of a computer system to perform the functions described above in accordance with embodiments of the present invention. Such logic may form part of a computer adapted to direct an information-processing device to perform the functions described above.

The data structures and code described herein may be partially or fully stored on a computer-readable storage medium and/or a hardware module and/or hardware apparatus. A computer-readable storage medium includes, but is not limited to, volatile memory, non-volatile memory, magnetic and optical storage devices or other media, now known or later developed, that are capable of storing code and/or data. Various circuit blocks of the embodiments of the present invention described above may be disposed in an application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), dedicated or shared processors, and/or other hardware modules or apparatuses now known or later developed.

The methods and processes described herein may be partially or fully embodied as code and/or data stored in a computer-readable storage medium or device, so that when a computer system reads and executes the code and/or data, the computer system performs the associated methods and processes. The methods and processes may also be partially or fully embodied in hardware modules or apparatuses, so that when the hardware modules or apparatuses are activated, they perform the associated methods and processes. The methods and processes disclosed herein may be embodied using a combination of code, data, and hardware modules or apparatuses.

The above descriptions of embodiments of the present invention are illustrative and not limitative. For example, the various embodiments of the present inventions are not limited to the use antennas, transmit and/or receive chain elements, cancellation circuits, radio frequency transceivers, transceivers, digital to analog converters, analog to digital converters. Other modifications and variations will be apparent to those skilled in the art and are intended to fall within the scope of the appended claims. 

What is claimed is:
 1. A method for detecting motion in an environment, comprising: transmitting a radio frequency signal; receiving a composite signal comprising a plurality of backscatter signals, wherein the plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment, wherein the one or more objects comprise at least one moving object and one or more static objects; and detecting the at least one moving object in accordance with the composite signal.
 2. The method of claim 1, wherein detecting the at least one moving object comprises: estimating one or more parameters corresponding to a plurality of backscatter signals corresponding to the at least one moving object, the one or more parameters being estimated by progressively removing one or more backscatter signals corresponding to the one or more static objects from the composite signal.
 3. The method of claim 2, wherein the one or more parameters are estimated for a plurality of time stamps during a first time period, the method further comprising: generating a plurality of image frames, each corresponding to one of the plurality of time stamps, each image frame having a plurality of pixels, each pixel corresponding to one of the plurality of backscatter signals; and identifying a plurality of associations between the plurality of pixels in the plurality of image frames.
 4. The method of claim 3, further comprising: identifying a plurality of paths corresponding to the at least one moving object in accordance with the plurality of associations.
 5. The method of claim 4, further comprising: determining one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object; and determining a number of moving objects corresponding to the number of motion categories.
 6. The method of claim 3, wherein the plurality of associations between the plurality of pixels in the plurality of image frames are identified by performing a Hungarian Algorithm.
 7. The method of claim 2, wherein the one or more parameters correspond to one or more of an angle of arrival of a backscatter signal at a receiver, attenuation, and delay since the radio frequency signal was transmitted.
 8. The method of claim 2, wherein the composite signal comprises a first portion received from a first antenna and a second portion received from a second antenna different from the first antenna, and the one or more parameters corresponding to the plurality of backscatter signals are estimated based at least on a difference between the first portion and second portion of the composite signal.
 9. The method of claim 1, further comprising: transmitting information corresponding to the one or more moving objects to a server; and receiving estimated location of the one or more moving objects from the server.
 10. A method for tracing motion, comprising: receiving information corresponding to one or more moving objects from one or more devices, wherein the information comprises one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects; estimating location of at least one of the one or more moving objects in accordance with the received information from the one or more devices.
 11. The method of claim 10, further comprising: identifying a plurality of paths corresponding to the at least one moving object in accordance with the received information.
 12. The method of claim 11, further comprising: determining one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object; and determining a number of moving objects corresponding to the number of motion categories.
 13. The method of claim 11, further comprising: tracing motion of the at least one moving object in accordance with the identified plurality of paths.
 14. The method of claim 10, wherein the one or more parameters comprise one or more of an angle of arrival of a backscatter signal at a device, attenuation of the backscatter signal, and a delay corresponding to the backscatter signal.
 15. The method of claim 10, wherein estimating the location comprises: estimating the location of at least one of the moving objects using extended Kalman filter.
 16. An apparatus for detecting motion in an environment, comprising: a transmitter; one or more receivers; a memory; and at least one processor coupled to the transmitter, the one or more receivers and the memory, the at least one processor configured to: transmit a radio frequency signal; receive a composite signal comprising a plurality of backscatter signals, wherein the plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment, wherein the one or more objects comprise at least one moving object and one or more static objects; and detect the at least one moving object in accordance with the composite signal.
 17. The apparatus of claim 16, wherein the at least one processor is further configured to: estimate one or more parameters corresponding to a plurality of backscatter signals corresponding to the at least one moving object, the one or more parameters being estimated by progressively removing one or more backscatter signals corresponding to the one or more static objects from the composite signal.
 18. The apparatus of claim 17, wherein the one or more parameters are estimated for a plurality of time stamps during a first time period, the at least one processor is further configured to: generate a plurality of image frames, each corresponding to one of the plurality of time stamps, each image frame having a plurality of pixels, each pixel corresponding to one of the plurality of backscatter signals; and identify a plurality of associations between the plurality of pixels in the plurality of image frames.
 19. The apparatus of claim 18, wherein the at least one processor is further configured to: identify a plurality of paths corresponding to the at least one moving object in accordance with the plurality of associations.
 20. The apparatus of claim 19, wherein the at least one processor is further configured to: determine one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object; and determine a number of moving objects corresponding to the number of motion categories.
 21. The apparatus of claim 18, wherein the plurality of associations between the plurality of pixels in the plurality of image frames are identified by performing a Hungarian Algorithm.
 22. The apparatus of claim 17, wherein the one or more parameters correspond to one or more of an angle of arrival of a backscatter signal at a receiver, attenuation, and delay since the radio frequency signal was transmitted.
 23. The apparatus of claim 17, wherein the composite signal comprises a first portion received from a first antenna and a second portion received from a second antenna different from the first antenna, and the one or more parameters corresponding to the plurality of backscatter signals are estimated based at least on a difference between the first portion and second portion of the composite signal.
 24. The apparatus of claim 16, wherein the at least one processor is further configured to: transmit information corresponding to the one or more moving objects to a server; and receive estimated location of the one or more moving objects from the server.
 25. An apparatus for tracing motion, comprising: one or more receivers; a memory; and at least one processor coupled to the one or more receivers and the memory, the at least one processor configured to: receive information corresponding to one or more moving objects from one or more devices, wherein the information comprises one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects; and estimate location of at least one of the one or more moving objects in accordance with the received information from the one or more devices.
 26. The apparatus of claim 25, wherein the at least one processor is further configured to: identify a plurality of paths corresponding to the at least one moving object in accordance with the received information.
 27. The apparatus of claim 26, wherein the at least one processor is further configured to: determine one or more motion categories by finding one or more associations between the identified plurality of paths and the at least one moving object; and determine a number of moving objects corresponding to the number of motion categories.
 28. The apparatus of claim 26, wherein the at least one processor is further configured to: trace motion of the at least one moving object in accordance with the identified plurality of paths.
 29. A non-transitory processor-readable medium for detecting motion in an environment, comprising processor-readable instructions configured to cause one or more processors to: transmit a radio frequency signal; receive a composite signal comprising a plurality of backscatter signals, wherein the plurality of the backscatter signals correspond to a reflection of the radio frequency signal from one or more objects in the environment, wherein the one or more objects comprise at least one moving object and one or more static objects; and detect the at least one moving object in accordance with the composite signal.
 30. A non-transitory processor-readable medium for tracing motion in an environment, comprising processor-readable instructions configured to cause one or more processors to: receive information corresponding to one or more moving objects from one or more devices, wherein the information comprises one or more parameters corresponding to one or more backscatter signals being reflected from the one or more moving objects; and estimate location of at least one of the one or more moving objects in accordance with the received information from the one or more devices. 